<p dir="ltr">Blood hemoglobin (Hgb) testing is a widely used clinical laboratory test for a variety of patient care needs. However, conventional blood Hgb measurements involve invasive blood sampling, exposing patients to potential risks and complications from needle pricks and iatrogenic blood loss. Although noninvasive blood Hgb quantification methods are under development, they still pose challenges in achieving performance comparable to clinical laboratory blood Hgb test results (i.e., gold standard). In particular, optical spectroscopy can provide reliable blood Hgb tests, but its practical utilizations in diagnostics are limited by bulky optical components, high costs, and extended data acquisition time. Mobile health (mHealth) or diagnostic colorimetric applications have a potential for point-of-care blood Hgb testing. However, achieving color accuracy for diagnostic applications is a complex matter, affected by device models, light conditions, and image file formats.</p><p dir="ltr">To address these limitations, we propose biophysics-based machine learning algorithms that combine hyperspectral learning and spectroscopic gamut-informed learning for accurate and precise mHealth blood Hgb assessments in a noninvasive manner. This method utilizes single-shot photographs of peripheral tissue acquired by onboard smartphone cameras. The palpebral conjunctiva (i.e., inner eyelid) serves as an ideal peripheral tissue site, owing to its easy accessibility, relatively uniform microvasculature, and absence of skin pigmentation (i.e., melanocytes). First, hyperspectral learning enables a mapping from red-green-blue (RGB) values of a digital camera into detailed hyperspectral information: an inverse mapping from a sparse space (tristimulus color values) to a dense space (multiple wavelengths). Hyperspectral learning employs a statistical learning framework to reconstruct a high-resolution spectrum from a digital photo of the palpebral conjunctiva, eliminating the need for complex and costly optical instrumentation. Second, comprehensive spectroscopic analyses of peripheral tissue are used to establish a unique blood Hgb gamut and design a diagnostic color reference chart highly sensitive to blood Hgb and peripheral perfusion. Informed by the domain knowledge of tissue optics and machine vision, the Hgb gamut-based learning algorithm offers device/light/format-agnostic color recovery of the palpebral conjunctiva, outperforming the existing color correction methods.</p><p dir="ltr">This mHealth blood Hgb prediction method exhibits comparable accuracy and precision to capillary blood sampling tests (e.g., finger prick) over a wide range of blood Hgb values, ensuring its reliability, consistency, and reproducibility. Importantly, by employing only a digital photograph with the Hgb gamut-learned color recovery, hyperspectral learning-based blood Hgb assessments allow noninvasive, continuous, and real-time reading of blood Hgb levels in resource-limited and at-home settings. Furthermore, our biophysics-based machine learning approaches for digital health applications can lay the foundation for the future of personalized medicine and facilitate the tempo of clinical translation, empowering individuals and frontline healthcare workers.</p>